Use 300m

README.md

@@ -10,7 +10,7 @@ tags:
 - generated_from_trainer
 - dataset_size:100000
 - loss:CachedMultipleNegativesRankingLoss
-base_model: google/embeddinggemma-300M
+base_model: google/embeddinggemma-300m
 widget:
 - source_sentence: 'What are the potential effects of stopping inhaled corticosteroid
     (ICS) therapy in patients with chronic obstructive pulmonary disease (COPD)?
@@ -987,7 +987,7 @@ metrics:
 - cosine_mrr@10
 - cosine_map@100
 model-index:
-- name: EmbeddingGemma-300M trained on the Medical Instruction and RetrIeval Dataset
+- name: EmbeddingGemma-300m trained on the Medical Instruction and RetrIeval Dataset
     (MIRIAD)
   results:
   - task:
@@ -1096,9 +1096,9 @@ model-index:
       name: Cosine Map@100
 ---
 
-# EmbeddingGemma-300M finetuned on the Medical Instruction and RetrIeval Dataset (MIRIAD)
+# EmbeddingGemma-300m finetuned on the Medical Instruction and RetrIeval Dataset (MIRIAD)
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300M](https://huggingface.co/google/embeddinggemma-300M) on the [miriad/miriad-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M) dataset (specifically the first 100.000 question-passage pairs from [tomaarsen/miriad-4.4M-split](https://huggingface.co/datasets/tomaarsen/miriad-4.4M-split)). It maps sentences & documents to a 768-dimensional dense vector space and can be used for medical information retrieval, specifically designed for searching for passages (up to 1k tokens) of scientific medical papers using detailed medical questions.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the [miriad/miriad-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M) dataset (specifically the first 100.000 question-passage pairs from [tomaarsen/miriad-4.4M-split](https://huggingface.co/datasets/tomaarsen/miriad-4.4M-split)). It maps sentences & documents to a 768-dimensional dense vector space and can be used for medical information retrieval, specifically designed for searching for passages (up to 1k tokens) of scientific medical papers using detailed medical questions.
 
 This model has been trained using code from our [EmbeddingGemma blogpost](https://huggingface.co/blog/embeddinggemma) to showcase how the EmbeddingGemma model can be finetuned on specific domains/tasks for even stronger performance. It is not affiliated with Google.
 
@@ -1106,7 +1106,7 @@ This model has been trained using code from our [EmbeddingGemma blogpost](https:
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [google/embeddinggemma-300M](https://huggingface.co/google/embeddinggemma-300M) <!-- at revision a3cd7d576fa223c646b6b3fb05d801d031ddd393 -->
+- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision a3cd7d576fa223c646b6b3fb05d801d031ddd393 -->
 - **Maximum Sequence Length:** 1024 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
@@ -1148,7 +1148,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("sentence-transformers/embeddinggemma-300M-medical")
+model = SentenceTransformer("sentence-transformers/embeddinggemma-300m-medical")
 # Run inference
 queries = [
     "What are some potential limitations in projecting the future demand for joint replacement surgeries?\n",